GLM API Pricing Explained: GLM-5.2 & GLM-4-Plus Costs, Peak Rates, and Billing

Understand GLM token pricing, peak/off-peak multipliers, and how to estimate your monthly AI spending — with real cost examples.

GLM (General Language Model) by Zhipu AI is one of China's leading large language model families. It offers strong Chinese-language understanding, competitive pricing, and an OpenAI-compatible API. This guide breaks down everything you need to know about GLM API pricing — including the unique peak/off-peak multiplier system.

GLM Model Lineup and Pricing

How GLM API Billing Works

GLM API calls are billed based on token consumption. A token is roughly 3/4 of an English word or 1-2 Chinese characters. You are billed separately for:

• Input tokens — the text you send to the model (your prompt + context)
• Output tokens — the text the model generates in response

GLM-5.2 Peak and Off-Peak Rate System

GLM-5.2 uses a unique time-based multiplier system. Your token consumption is multiplied by a rate depending on when you make the call:

Practical Cost Example

Say a GLM-5.2 API call uses 500 input tokens and 1,000 output tokens (1,500 total):

GLM-4-Plus: Simple Flat Pricing

Unlike GLM-5.2, GLM-4-Plus uses simple flat-rate pricing with no multipliers:

• $0.70 per 1M input tokens
• $0.70 per 1M output tokens
• No peak/off-peak surcharges

This makes GLM-4-Plus easier to budget for and predict costs. For most production applications, GLM-4-Plus is the recommended choice.

GLM-4-Plus Cost Examples

GLM vs Other AI APIs: Price Comparison

For a full comparison, see our AI model comparison page or the cheapest AI API guide.

How to Minimize GLM API Costs

1. Avoid Peak Hours for GLM-5.2

Batch non-urgent tasks outside 14:00-18:00 Beijing time. During peak hours, the same request costs 50% more than off-peak (3x vs 2x multiplier). For GLM-4-Plus, this is not an issue since it uses flat pricing.

2. Use GLM-4-Plus Instead of GLM-5.2

GLM-4-Plus uses flat pricing with no multipliers, making it easier to budget and often cheaper overall. Unless you specifically need GLM-5.2's capabilities, GLM-4-Plus is the safer choice.

3. Optimize Prompt Length

Input tokens are billed. Trim unnecessary context, use concise system prompts, and avoid sending redundant information. A 1,000-token system prompt costs the same as a 100-token one — but adds up at scale.

4. Set max_tokens Appropriately

Don't request 4,096 output tokens if you only need 200. Set max_tokens to a reasonable limit to prevent runaway costs from unexpectedly long responses.

5. Cache Repeated Queries

If your application sends identical queries frequently, cache the responses to avoid paying for the same computation multiple times. This is especially effective for FAQ bots and classification pipelines.

6. Use GLM-4-Flash for Testing

GLM-4-Flash is free for basic usage. Use it during development and testing to iterate without spending. Switch to GLM-4-Plus for production once your code is stable.

Code Example: Calling GLM API

from openai import OpenAI

client = OpenAI(
    api_key="your-token1-us-key",
    base_url="https://discount-token.com/v1"
)

# Call GLM-4-Plus
response = client.chat.completions.create(
    model="glm-4-plus",
    messages=[
        {"role": "system", "content": "你是一个专业的中文写作助手。"},
        {"role": "user", "content": "写一首关于春天的诗。"}
    ],
    max_tokens=500
)

print(response.choices[0].message.content)
print(f"Tokens: {response.usage.total_tokens}")

Estimating Monthly Costs

Use our interactive cost calculator to estimate your monthly spending. Here are typical GLM usage patterns:

Rate Limits

Frequently Asked Questions

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